/** Train L2 tree boost. */
private void train2(Attribute[] attributes, double[][] x, int[] y) {
int n = x.length;
int N = (int) Math.round(n * f);
int[] y2 = new int[n];
for (int i = 0; i < n; i++) {
if (y[i] == 1) {
y2[i] = 1;
} else {
y2[i] = -1;
}
}
y = y2;
double[] h = new double[n]; // current F(x_i)
double[] response = new double[n]; // response variable for regression tree.
double mu = Math.mean(y);
b = 0.5 * Math.log((1 + mu) / (1 - mu));
for (int i = 0; i < n; i++) {
h[i] = b;
}
int[][] order = SmileUtils.sort(attributes, x);
RegressionTree.NodeOutput output = new L2NodeOutput(response);
trees = new RegressionTree[T];
int[] perm = new int[n];
int[] samples = new int[n];
for (int i = 0; i < n; i++) {
perm[i] = i;
}
for (int m = 0; m < T; m++) {
Arrays.fill(samples, 0);
Math.permutate(perm);
for (int i = 0; i < N; i++) {
samples[perm[i]] = 1;
}
for (int i = 0; i < n; i++) {
response[i] = 2.0 * y[i] / (1 + Math.exp(2 * y[i] * h[i]));
}
trees[m] = new RegressionTree(attributes, x, response, J, order, samples, output);
for (int i = 0; i < n; i++) {
h[i] += shrinkage * trees[m].predict(x[i]);
}
}
}
